Not a generic fine-tune. We design the architecture, curate the training data, align with RLHF, quantize, and deploy — on-prem or cloud. Full LLM development services from discovery to production. Fixed fee. You own the weights.
LLM development services are the complete engineering work required to design, train, align, and deploy a large language model built specifically for your organisation's data, domain, and tasks — delivered by an external specialist team with the ML infrastructure, GPU compute, and evaluation methodology to ship production-grade models.
Three figures that explain why enterprise teams are moving from API access to specialist LLM development services.
Every Modulus engagement delivers shipped, production-running software. These are the eight service areas covered across a full LLM development engagement.
We evaluate your use case across six dimensions — parameter budget for your latency target, inference hardware constraints, required context window, domain distance from existing model knowledge, licensing requirements, and multilingual needs — before selecting the architecture. For most enterprise projects, we compare two to three candidate base models on a sample task before committing. The selection is documented in a written architecture decision record that becomes part of your project deliverables. This stage alone prevents the most expensive error in LLM development: training the wrong model for weeks before discovering the architecture cannot meet the latency or accuracy requirements.
Raw documents are rarely training-ready. We build a reproducible data pipeline covering ingestion from your sources (PDFs, HTML, databases, APIs, internal tools), deduplication using MinHash LSH, quality filtering with perplexity scoring and rule-based heuristics, format conversion to instruction-following or causal language modeling format, and train/validation/test splitting with stratification. For instruction tuning datasets, we draft, refine, and validate instruction templates with your domain experts before training begins. The entire pipeline is versioned and handed over at project close so you can retrain on new data independently.
Depending on your data volume and domain distance from existing models, we apply continual pre-training on your corpus to shape the model's core knowledge, followed by supervised fine-tuning (SFT) on instruction-response pairs to align output format and domain behavior. Continual pre-training is run using DeepSpeed ZeRO or FSDP on distributed GPU clusters, with full experiment tracking via Weights and Biases or MLflow. Every training run produces checkpoints evaluated against your domain eval harness, so regressions are caught within hours rather than discovered at delivery. LoRA and QLoRA are applied when parameter efficiency is a priority — for example, when the base model is large (70B+) and the adaptation budget is constrained.
When your use case requires enforced output quality standards — citation accuracy in a legal tool, tone consistency in a customer-facing assistant, safety refusals in a healthcare application — we apply preference alignment using Direct Preference Optimization (DPO) or a full RLHF pipeline with a trained reward model. DPO is our default for most enterprise use cases: it is simpler to implement, more stable to train, and produces strong alignment results without the complexity of a separate reward model training run. Full RLHF with a reward model is applied when you have the capacity to generate human preference data at scale (500+ rated response pairs) and the use case warrants the additional alignment depth.
A 70B parameter model in full precision requires approximately 140 GB of GPU VRAM — impractical for most on-premises inference budgets. We apply GPTQ or AWQ quantization (4-bit or 8-bit, depending on accuracy tolerance) to produce a model that fits your available hardware while maintaining accuracy within the agreed tolerance on your domain eval set. For throughput-critical applications, we also implement speculative decoding using a small draft model to accelerate generation speed without quality degradation. Continuous batching is configured in the inference server to maximise tokens-per-second under your expected concurrent query load. All quantization decisions are documented and the final accuracy delta versus the full-precision checkpoint is reported explicitly.
Most production LLM deployments require the model to answer questions about knowledge that changes after training — internal policies, current product documentation, live operational data. We build a RAG layer on top of your custom LLM using a hybrid retrieval stack: dense vector search (Qdrant or pgvector), sparse keyword search (BM25), reciprocal rank fusion merging, and a cross-encoder re-ranker. Query rewriting and HyDE are implemented to improve retrieval precision on conversational inputs. The RAG pipeline keeps your knowledge base current without retraining, and every generated answer includes source citations traceable to specific document chunks.
Six stages with defined deliverables and approval gates. No stage begins until the prior stage is approved.
Every engagement starts with a free discovery call to map your use case, data estate, infrastructure, and success criteria. We ask precise questions about your corpus volume and quality, target inference hardware, latency requirements, compliance constraints, and the specific tasks the model must perform. Within 48 hours of this call we deliver a fixed-price proposal scoping the full engagement: base model recommendation with rationale, training approach, evaluation methodology, deliverable list, and timeline. There are no surprises after signing — the fixed fee covers the full scope including any additional training iterations required to hit the agreed eval thresholds.
Before any training work begins, we audit your corpus across five quality dimensions — cleanliness, diversity, domain relevance, format consistency, and volume adequacy for the chosen training approach. The audit produces a written data readiness report with a remediation plan if preprocessing is required. Critically, we also build the domain-specific evaluation harness during this stage: a golden dataset of held-out question-answer pairs, format-compliance checks, adversarial prompts, and where applicable, retrieval-grounding accuracy tests. Locking the eval framework before training begins is what prevents the post-hoc rationalization of mediocre results that plagues many ML projects — we agree what "good" looks like before we run a single training step.
We build the full ingestion and preprocessing pipeline from your raw data sources to training-ready format. This includes: document parsers for each source type, a deduplication pass using MinHash locality-sensitive hashing at scale, quality filtering with perplexity-based scoring and domain-specific rules, tokenizer validation to confirm the base model's vocabulary adequately covers your domain terminology, and train/validation/test splitting. For instruction tuning, we build and validate the instruction templates with your domain experts before generating the full dataset. All pipeline code is written in Python with full documentation and is included in the final IP transfer.
Training runs are executed on GPU clusters (A100 or H100 where available) using the frameworks appropriate to your model size and training type: DeepSpeed ZeRO Stage 2 or 3 for large continual pre-training runs, FSDP for mid-size runs, and Hugging Face TRL with PEFT for fine-tuning. Every run is instrumented with full experiment tracking — loss curves, gradient norms, learning rate schedules, evaluation scores at checkpoints — accessible to your team in real time via a shared W&B or MLflow workspace. Alignment (DPO or RLHF) follows the base training run and is also evaluated against the domain harness at each iteration. We surface any accuracy-latency trade-off decisions for your approval before committing to a final checkpoint.
The final trained checkpoint undergoes quantization using GPTQ or AWQ (or GGUF for CPU-inference targets). We run a quantization accuracy evaluation comparing the quantized model against the full-precision checkpoint on your eval harness and report the delta explicitly. If accuracy loss exceeds the agreed tolerance, we adjust the quantization configuration — typically moving from 4-bit to 8-bit, or applying mixed-precision quantization at sensitive layers — before proceeding. The final model must clear all agreed accuracy thresholds on the domain eval harness and meet the latency SLA on your target hardware before we consider the training phase complete.
Deployment uses vLLM, Text Generation Inference, or Ollama depending on your infrastructure and operational preferences. We configure the inference server for continuous batching and (where applicable) speculative decoding, expose an OpenAI-compatible REST API, and set up full observability: Prometheus metrics (latency p50/p95/p99, tokens per second, error rate, cache hit rate) feeding a Grafana dashboard with alerts on latency regressions and output distribution drift. The 30 to 60-day post-launch support window covers live traffic tuning, prompt engineering adjustments, and any batched re-inference runs needed to validate production accuracy. At close, IP transfer delivers: all model weights and checkpoints, training scripts and config files, data pipeline code, evaluation harness, deployment configuration, and full technical documentation. You own everything — no licensing fees, no ongoing vendor dependency.
The most common reason enterprise LLM projects produce disappointing results is that success was never defined precisely enough before development began. We solve this by locking the evaluation framework before any training compute is spent.
Every LLM development engagement begins with the construction of a domain-specific evaluation harness before any model training begins. This harness becomes the contractual definition of project success.
The post-hoc rationalization of training results is endemic in ML services work. A vendor that defines success criteria after seeing what the model produces can always find a framing in which the results look acceptable. A vendor that locks success criteria before training begins is accountable to an objective standard that does not change based on what was achievable. This is not just a quality assurance best practice — it is the mechanism by which fixed-fee LLM development work is made commercially viable for the vendor and risk-free for the client. When success is precisely defined upfront, the scope of rework is bounded and predictable. When success is defined vaguely, every iteration requires a renegotiation of what "good enough" means — which is how projects overrun budgets and timelines.
The most valuable custom LLM deployments share a common characteristic: a large proprietary corpus of domain text that general commercial models have never seen.
Contract analysis, clause extraction, case research summarisation, and regulatory filing review. Legal LLMs benefit from training on proprietary case precedent and internal contract archives that define firm-specific risk interpretation.
Compliance document parsing, regulatory report generation, risk analysis across structured and unstructured data, and internal research summarisation. Data residency requirements make on-prem deployment essential in most regulated jurisdictions.
Clinical note summarisation, ICD and CPT coding assistance, clinical trial data extraction, and medical literature synthesis. HIPAA-compliant on-prem deployment is a hard requirement — commercial API access is not viable.
Equipment maintenance documentation assistants, fault diagnosis from sensor logs and maintenance histories, supplier communication automation, and technical specification search across legacy document archives.
Internal knowledge base assistants, code generation for proprietary frameworks and APIs, technical support automation on proprietary products, and documentation search across large internal engineering wikis.
Proposal and engagement letter generation from historical project archives, client communication drafting, and research synthesis for consulting, accounting, and advisory firms with large proprietary methodology libraries.
| Dimension | Generic API (GPT-4o, Claude) | Modulus LLM Development Services |
|---|---|---|
| Data privacy | ✗ Your data processed on vendor servers | ✓ Air-gapped, on-prem option |
| Latency control | ~ Shared infrastructure, vendor-controlled | ✓ Dedicated, tunable to your SLA |
| Cost at scale | ✗ Per-token, compounds fast at enterprise volume | ✓ Fixed infrastructure cost after delivery |
| Domain accuracy | ~ Generalist ceiling, prompt-dependent | ✓ Trained on your proprietary corpus |
| IP ownership | ✗ Provider owns the model entirely | ✓ You own all weights, code, and evals |
| Vendor dependency | ✗ Pricing changes, API deprecations, outages | ✓ Zero dependency after delivery |
| Regulatory compliance | ✗ Vendor-dependent, jurisdiction risk | ✓ Full control of data flow and residency |
| Custom vocabulary | ✗ Prompt engineering only — superficial | ✓ Baked into weights through training |
A regional fund administrator needed to automate regulatory correspondence review across three jurisdictions with distinct compliance vocabulary. Staff manually reviewed approximately 1,200 documents per month. A GPT-4-based wrapper was piloted but produced unacceptable jurisdiction-mixing errors on 22% of test documents and introduced data residency concerns that the fund's legal counsel flagged as a blocking issue.
Modulus ran continual pre-training on 31 GB of regulatory filings, internal compliance manuals, and historical correspondence, followed by SFT on 3,800 hand-verified instruction examples and DPO alignment on jurisdiction-preference pairs. Deployed via vLLM on a 4× A100 on-premises cluster. Domain eval harness: 960 held-out documents across six document types and three jurisdictions.
Tools are selected to fit the project — not applied as a fixed template. These are the frameworks we work with across every layer of a full LLM development engagement.
Every engagement is scoped after a free discovery call. Fixed-price proposal delivered within 48 hours. No time-and-materials billing — ever.
Free discovery call. Fixed-price proposal within 48 hours. LLM development services with full IP transfer — weights, code, and evaluation suite.
Tell us your use case and data situation. Fixed-price proposal within 48 hours.